pages/3_🕮_Docs_Demo.py

import os
import streamlit as st

os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]

# +

st.set_page_config(page_title="Protected Areas Database Chat", page_icon="🦜")
st.title("Protected Areas Database Chat")

st.markdown('''

This Chatbot is designed only to answer questions based on [PAD Technical How-Tos](https://www.protectedlands.net/pad-us-technical-how-tos/).  The Chatbot will refuse to answer questions outside of this context.

Example queries:

- What is the difference between Fee and Easements?
- What do the gap status codes mean?
''')
# -

# optional
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_chroma import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.llms import Ollama


from langchain_openai import ChatOpenAI

# +
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

# Setup LLM and QA chain

models = {"chatgpt3.5": ChatOpenAI(model="gpt-3.5-turbo", temperature=0, api_key=st.secrets["OPENAI_API_KEY"], streaming=True),
#           "chatgpt4": ChatOpenAI(model="gpt-4", temperature=0, api_key=st.secrets["OPENAI_API_KEY"]),
          "phi3": Ollama(model="phi3", temperature=0),
          "duckdb-nsql": Ollama(model="duckdb-nsql", temperature=0),
          "command-r-plus": Ollama(model="command-r-plus", temperature=0),
          "mistral":  Ollama(model="mistral", temperature=0),
          "wizardlm2":  Ollama(model="wizardlm2", temperature=0),
          "sqlcoder": Ollama(model="sqlcoder", temperature=0),
          "zephyr": Ollama(model="zephyr", temperature=0),
          "gemma": Ollama(model="gemma", temperature=0),
          "llama3": Ollama(model="llama3", temperature=0),
         }


with st.sidebar:

    "Non-ChatGPT models assume you are running the app locally with ollama installed."
    choice = st.radio("Select an LLM:", models)
    llm = models[choice]

# -

# Load, chunk and index the contents of the blog.
loader = WebBaseLoader(
    web_paths=(["https://www.protectedlands.net/pad-us-technical-how-tos/",
                "https://www.protectedlands.net/uses-of-pad-us/",
#                "https://www.protectedlands.net/pad-us-data-structure-attributes/"
               ]),
    bs_kwargs=dict(
        parse_only=bs4.SoupStrainer(
            class_=("main-body")
        )
    ),
)
docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=400)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())

# Retrieve and generate using the relevant snippets of the blog.
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain.invoke("What is the difference between Fee and Easement?")


# +

from langchain_core.runnables import RunnableParallel

rag_chain_from_docs = (
    RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain_with_source = RunnableParallel(
    {"context": retriever, "question": RunnablePassthrough()}
).assign(answer=rag_chain_from_docs)

rag_chain_with_source.invoke("What is the difference between Fee and Easement?")

# +
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain

# Setup memory for contextual conversation
msgs = StreamlitChatMessageHistory()
memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=msgs, return_messages=True)

#qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory, verbose=True)

if len(msgs.messages) == 0 or st.sidebar.button("Clear message history"):
    msgs.clear()
    msgs.add_ai_message("How can I help you?")

avatars = {"human": "user", "ai": "assistant"}
for msg in msgs.messages:
    st.chat_message(avatars[msg.type]).write(msg.content)

if user_query := st.chat_input(placeholder="Ask me about PAD!"):
    st.chat_message("user").write(user_query)

    with st.chat_message("assistant"):
        response = rag_chain.invoke(user_query)
        response